# lppm

##### Fit Point Process Model to Point Pattern on Linear Network

Fit a point process model to a point pattern dataset on a linear network

##### Usage

`lppm(X, ...)`## S3 method for class 'formula':
lppm(X, interaction=NULL, ..., data=NULL)

## S3 method for class 'lpp':
lppm(X, ..., eps=NULL, nd=1000)

##### Arguments

- X
- Either an object of class
`"lpp"`

specifying a point pattern on a linear network, or a`formula`

specifying the point process model. - ...
- Arguments passed to
`ppm`

. - interaction
- An object of class
`"interact"`

describing the point process interaction structure, or`NULL`

indicating that a Poisson process (stationary or nonstationary) should be fitted. - data
- Optional. The values of spatial covariates (other than the Cartesian coordinates) required by the model. A list whose entries are images, functions, windows, tessellations or single numbers.
- eps
- Optional. Spacing between dummy points along each segment of the network.
- nd
- Optional. Number of dummy points equally spaced along each segment
of the network. Ignored if
`eps`

is given.

##### Details

This function fits a point process model to data that specify
a point pattern on a linear network. It is a counterpart of
the model-fitting function `ppm`

designed
to work with objects of class `"lpp"`

instead of `"ppp"`

.

The function `lppm`

is generic, with methods for
the classes `formula`

and `lppp`

.

In `lppm.lpp`

the first argument `X`

should be an object of class `"lpp"`

(created by the command `lpp`

) specifying a point pattern
on a linear network.

In `lppm.formula`

,
the first argument is a `formula`

in the Rlanguage
describing the spatial trend model to be fitted. It has the general form
`pattern ~ trend`

where the left hand side `pattern`

is usually
the name of a point pattern on a linear network
(object of class `"lpp"`

)
to which the model should be fitted, or an expression which evaluates
to such a point pattern;
and the right hand side `trend`

is an expression specifying the
spatial trend of the model.

Other arguments `...`

are passed from `lppm.formula`

to `lppm.lpp`

and from `lppm.lpp`

to `ppm`

.

##### Value

- An object of class
`"lppm"`

representing the fitted model. There are methods for`print`

,`predict`

,`coef`

and similar functions.

##### References

Ang, Q.W. (2010)
*Statistical methodology for events on a network*.
Master's thesis, School of Mathematics and Statistics, University of
Western Australia.
Ang, Q.W., Baddeley, A. and Nair, G. (2012)
Geometrically corrected second-order analysis of
events on a linear network, with applications to
ecology and criminology.
*Scandinavian Journal of Statistics* **39**, 591--617.

McSwiggan, G., Nair, M.G. and Baddeley, A. (2012) Fitting Poisson point process models to events on a linear network. Manuscript in preparation.

##### See Also

##### Examples

```
example(lpp)
lppm(X ~1)
lppm(X ~x)
```

*Documentation reproduced from package spatstat, version 1.42-2, License: GPL (>= 2)*